Influence in Classification via Cooperative Game Theory
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 511-516.
Abstract
A dataset has been classified by some unknown classifier into two types of points. What were the most important factors in determining the classification outcome? In this work, we employ an axiomatic approach in order to uniquely characterize an influence measure: a function that, given a set of classified points, outputs a value for each feature corresponding to its influence in determining the classification outcome. We show that our influence measure takes on an intuitive form when the unknown classifier is linear. Finally, we employ our influence measure in order to analyze the effects of user profiling on Google's online display advertising.
@inproceedings{ijcai2015p78,
title = {Influence in Classification via Cooperative Game Theory},
author = {Datta, Amit and Datta, Anupam and Procaccia, Ariel D. and Zick, Yair},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {511--516},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/78},
}
TY - CPAPER TI - Influence in Classification via Cooperative Game Theory AU - Datta, Amit AU - Datta, Anupam AU - Procaccia, Ariel D. AU - Zick, Yair T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 511 EP - 516 UR - https://ijcai.org/proceedings/2015/78 ER -Copied
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